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Our understanding of how the collaborative efforts spent by teams relate to their performance is still a matter of debate. Teamwork results in a highly interconnected ecosystem of potentially overlapping components where tasks are performed in interaction with team members and across other teams. To tackle this problem, we propose a graph neural network model to predict a team\u2019s performance while identifying the drivers determining such outcome. In particular, the model is based on three architectural channels: topological, centrality, and contextual, which capture different factors potentially shaping teams\u2019 success. We endow the model with two attention mechanisms to boost model performance and allow interpretability. A first mechanism allows pinpointing key members inside the team. A\u00a0second mechanism allows us to quantify the contributions of the three driver effects in determining the outcome performance. 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